LightGBM-Based Fault Diagnosis of Rotating Machinery Under Changing Working Conditions Using Modified Recursive Feature Elimination
This article presents an intelligent and accurate framework for fault diagnosis of induction motors using light gradient boosting machine (LightGBM). The proposed framework offers promising generalization ability when the testing data contains new unseen operating conditions unavailable during the t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9847250/ |
| _version_ | 1828153804028116992 |
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| author | Alireza Nemat Saberi Anouar Belahcen Jan Sobra Toomas Vaimann |
| author_facet | Alireza Nemat Saberi Anouar Belahcen Jan Sobra Toomas Vaimann |
| author_sort | Alireza Nemat Saberi |
| collection | DOAJ |
| description | This article presents an intelligent and accurate framework for fault diagnosis of induction motors using light gradient boosting machine (LightGBM). The proposed framework offers promising generalization ability when the testing data contains new unseen operating conditions unavailable during the training process. After the acquisition of vibration signals and feature extraction in multiple domains, we perform an iterative feature selection (FS) approach by utilizing a modified version of recursive feature elimination (RFE) and the features’ importance scores obtained by LightGBM. To prevent overfitting and subsequent selection bias, an outer resampling loop encompasses the whole process of our RFE-LightGBM algorithm. Moreover, instead of the conventional resampling methods based on K-fold cross-validation (CV) or leave-one-out CV (LOOCV), we use a new scheme called leave-one-loading-out CV (<italic>LOLO-CV</italic>). Leveraging <italic>LOLO-CV</italic>, the proposed FS method identifies the optimal feature subset, making the fault diagnosis robust under changing operating conditions. Then, the final classification is performed with optimal feature subset by training a new LightGBM model with adjusted hyperparameters employing Bayesian optimization. Experimental results from two real case studies show that our proposed fault diagnosis framework achieves accuracies between 98.55% and 100% for various testing scenarios. For example, for the worst-case testing scenario in the bearing dataset of Case Western Reserve University where the no-load data (0hp) is absent during the training process and is only used for testing, the testing accuracy of LightGBM classifier before and after applying the proposed RFE-LightGBM-FS method is 88.04% to 97.23%, respectively. Using the Bayesian hyperparameter optimization further improves the accuracy to 98.55%. |
| first_indexed | 2024-04-11T22:29:58Z |
| format | Article |
| id | doaj.art-25bbe80d6a5541c5ae0b1402cb2a27b6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T22:29:58Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-25bbe80d6a5541c5ae0b1402cb2a27b62022-12-22T03:59:26ZengIEEEIEEE Access2169-35362022-01-0110819108192510.1109/ACCESS.2022.31959399847250LightGBM-Based Fault Diagnosis of Rotating Machinery Under Changing Working Conditions Using Modified Recursive Feature EliminationAlireza Nemat Saberi0https://orcid.org/0000-0002-1327-468XAnouar Belahcen1https://orcid.org/0000-0003-2154-8692Jan Sobra2https://orcid.org/0000-0002-4865-8819Toomas Vaimann3https://orcid.org/0000-0003-0481-5066Department of Electrical Engineering and Automation, Aalto University, Aalto, FinlandDepartment of Electrical Engineering and Automation, Aalto University, Aalto, FinlandFaculty of Electrical Engineering, University of West Bohemia, Pilsen 3, Czech RepublicDepartment of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, Tallinn, EstoniaThis article presents an intelligent and accurate framework for fault diagnosis of induction motors using light gradient boosting machine (LightGBM). The proposed framework offers promising generalization ability when the testing data contains new unseen operating conditions unavailable during the training process. After the acquisition of vibration signals and feature extraction in multiple domains, we perform an iterative feature selection (FS) approach by utilizing a modified version of recursive feature elimination (RFE) and the features’ importance scores obtained by LightGBM. To prevent overfitting and subsequent selection bias, an outer resampling loop encompasses the whole process of our RFE-LightGBM algorithm. Moreover, instead of the conventional resampling methods based on K-fold cross-validation (CV) or leave-one-out CV (LOOCV), we use a new scheme called leave-one-loading-out CV (<italic>LOLO-CV</italic>). Leveraging <italic>LOLO-CV</italic>, the proposed FS method identifies the optimal feature subset, making the fault diagnosis robust under changing operating conditions. Then, the final classification is performed with optimal feature subset by training a new LightGBM model with adjusted hyperparameters employing Bayesian optimization. Experimental results from two real case studies show that our proposed fault diagnosis framework achieves accuracies between 98.55% and 100% for various testing scenarios. For example, for the worst-case testing scenario in the bearing dataset of Case Western Reserve University where the no-load data (0hp) is absent during the training process and is only used for testing, the testing accuracy of LightGBM classifier before and after applying the proposed RFE-LightGBM-FS method is 88.04% to 97.23%, respectively. Using the Bayesian hyperparameter optimization further improves the accuracy to 98.55%.https://ieeexplore.ieee.org/document/9847250/Electrical machinesbearingsfault diagnosisfeature importancegradient boostinghyperparameter optimization |
| spellingShingle | Alireza Nemat Saberi Anouar Belahcen Jan Sobra Toomas Vaimann LightGBM-Based Fault Diagnosis of Rotating Machinery Under Changing Working Conditions Using Modified Recursive Feature Elimination IEEE Access Electrical machines bearings fault diagnosis feature importance gradient boosting hyperparameter optimization |
| title | LightGBM-Based Fault Diagnosis of Rotating Machinery Under Changing Working Conditions Using Modified Recursive Feature Elimination |
| title_full | LightGBM-Based Fault Diagnosis of Rotating Machinery Under Changing Working Conditions Using Modified Recursive Feature Elimination |
| title_fullStr | LightGBM-Based Fault Diagnosis of Rotating Machinery Under Changing Working Conditions Using Modified Recursive Feature Elimination |
| title_full_unstemmed | LightGBM-Based Fault Diagnosis of Rotating Machinery Under Changing Working Conditions Using Modified Recursive Feature Elimination |
| title_short | LightGBM-Based Fault Diagnosis of Rotating Machinery Under Changing Working Conditions Using Modified Recursive Feature Elimination |
| title_sort | lightgbm based fault diagnosis of rotating machinery under changing working conditions using modified recursive feature elimination |
| topic | Electrical machines bearings fault diagnosis feature importance gradient boosting hyperparameter optimization |
| url | https://ieeexplore.ieee.org/document/9847250/ |
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